Heightened realism for computer-controlled units in real-time activity simulation

ABSTRACT

Enhanced realism of a real-time simulator having multiple computer-controlled units results from making the units capable of reacting to only those other units that each of them can be aware of because of their spatial relationships to the unit. Awareness is based upon probabilities; it can persist after a relationship changes; and it can be influenced by a unit&#39;s designation of a target. Each unit selects a target based upon a score incorporating multiple aspects of its tactical situation, and can change targets when the situation changes. A unit selects a strategy in response to which of a set of tactical configurations exist between the unit and its target; the strategy can change short of completion when the configuration changes. A plan produces guidance commands from the high-level strategy. The guidance commands are converted into control settings for guiding the subject unit using a physics engine for simulating the physical dynamics of the unit. The control settings can interact with each other under certain conditions. The time rate at which each computer-controlled unit performs the above operations varies with the tactical situation, and can be different for different units. The operator for each unit precomputes some data required for multiple calculations in performing the operations.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. application Ser. Nos. 09/099,573,filed Jun. 19, 1998 (now U.S. Pat. No. 6,110,215), entitled “HeightenedRealism for Computer-Controlled Units in Real-Time Activity Simulation”;Ser. No. 09/099,923, filed Jun. 19, 1998 (now U.S. Pat. No. 6,195,626),entitled “Heightened Realism for Computer-Controlled Units in Real-TimeActivity Simulation”; Ser. No. 09/100,694, filed Jun. 19, 1998 (now U.S.Pat. No. 6,443,733), entitled “Heightened Realism forComputer-Controlled Units in Real-Time Activity Simulation”; Ser. No.09/099,703, filed Jun. 19, 1998 (now U.S. Pat. No. 6,199,030), entitled“Heightened Realism for Computer-Controlled Units in Real-Time ActivitySimulation”; and Ser. No. 09/100,502, filed Jun. 19, 1998 (now U.S. Pat.No. 6,179,618), entitled “Heightened Realism for Computer-ControlledUnits in Real-Time Activity Simulation”.

BACKGROUND OF THE INVENTION

The present invention relates to electronic data processing, and moreparticularly concerns the real-time simulation of skill-based activitiessuch as air-to-air combat.

Designing a real-time air-combat simulator is full of challenges. One ofthem is developing a compelling artificial intelligence (AI) forallowing the computer to control the actions of simulated aircraft andother entities that try to shoot the human player out of the sky. (Forbrevity, all such entities will be referred to as “AI units.”)

Real-time simulators demand the utmost from a computer. There is neverenough time to perform all the calculations required to simulate themotions of the player's aircraft, other aircraft visible to the player,and complex terrain, and to render their images on a display screen.Where compromises are necessary, conventional simulators tend to skimpon the realism of the AI units. The purpose of the AI units is to mimichuman-like behavior as closely as possible. Playing present air-combatproducts quickly reveals weaknesses in the behavior of simulated enemypilots. They feel computerish. They perform in ways which are bothbetter and worse than human pilots. Their incredible ability to react toaircraft dead astern of them and to maneuver instantaneously isfrustrating to players. On the other hand, human players quickly learnto take advantage of their simplistic tactics, turning the game into aturkey shoot.

Conventional AI-unit simulators lack realism in the area of situationalawareness, the ability to see and thus react to other units in thesimulated region. The common expedient in air combat is to maintain alist of the three-dimensional position of every aircraft in the combatzone, and to make the full list available to every AI unit for targetselection, attack planning, and evasive maneuvers. This approach lends adisconcerting omniscience to their ability to track large numbers ofother aircraft in furballs, where many aircraft engage each other fromvarious attitudes. Real pilots can see any aircraft in a cone ahead ofthem, but awareness drops off for aircraft approaching from the flank orfrom above and below. Bogeys at six o'clock should be as invisible to AIunits as they are to human pilots. Another aspect of situationalawareness concerns persistence. Even though another aircraft is lessvisible out of the forward cone, human pilots remain aware of otheraircraft that drop astern from a sector of higher visibility, andcontinue to react to their presence for some period of time. Further, itis characteristic of human pilots to fixate upon a targeted aircraft,paying more attention to it and paying less attention to other aircraft.It should be easier for the human player to jump a simulated enemy unitwhen it is pursuing another target. It should be harder for the humanplayer to duck a pursuing enemy AI unit.

Target selection is also a shortcoming of conventional simulators. Whenmany targets are available in a furball, present-day AI units employcrude or even random factors for designating one of them to engage. And,once locked on to a specific target, contemporary AI units doggedlypursue it, refusing to acquire a more favorable target. This defectoften allows human players to consider otherwise suicidal tactics toapproach an enemy unit within point-blank firing range.

A shortcoming in the basic design of simulators for many-unit simulatorsis to enhance the realism of the player-controlled aircraft or otherunit at the expense of the computer-controlled units. For example, eachAI unit might be allocated the same fixed amount of computer resources,or all AI units given exactly the same skill level. Where manycomputer-controlled units are attempted, it is easy to justify skimpingon the physics of their simulation. Whereas the player's unit issimulated with a full physics engine that mimics many details of theunit's actual response to their control inputs, the AI units are guidedthrough simpler simulation techniques, usually through more or lessdirect control of their flight paths. The player's unit gets the fulltreatment; the AI units do not, and suffer for it.

Enhancing the realism of computer-controlled AI units is notstraightforward. The first problem is, of course, is to design theirbehaviors to be more realistic, as discussed above. Then, given the,computation-intensive nature of real-time simulation, the next problemis to implement those behaviors more efficiently. Simulators operate incycles, updating the current state of objects in the system during eachcycle to produce a new state for the next cycle. Conventionally, theparameters of the AI units are recalculated in a think interval thatremains constant for all parameters and that is the same for all units.In a complex simulation involving increased computation for many units,each of which can be in different circumstances, this simple schemewastes valuable time and resources. Slowly changing parameters areupdated more often than necessary, while units in the thick of theaction react sluggishly for lack of timely updates.

Realism of AI units suffers further from crude simulation of theirmaneuvers. Newer products in this genre employ sophisticated physicsengines for recreating the actual characteristics of the player'saircraft. Yet the computer-controlled units remain directed by simplercommands to proceed in a certain direction at a certain speed. Thistechnique results in jerky, mechanical actions, and in physicallyimpossible maneuvers. Vectoring routines can include artificialconstraints such as limits on turn rates, but the results are often lessthan believable. Also, the direct control used in conventionalsimulators allows little in the way of modeling different enemyaircraft. Although the user of a player-controlled simulator such asMicrosoft Flight Simulator® can choose among a wide variety of aircrafttypes, combat-type simulators are much more restricted. Thecomputer-controlled AI units are either fixed or restricted to a fewtypes having unrealistic behaviors.

Unrealistic actions are most noticeable during extreme maneuvers of anAI unit. For example, a computer-generated tactical command for anaircraft to descend sharply while in a bank may produce a totallyundesired maneuver, directing the craft in a wholly different directionthan that intended. Even in the absence of guidance commands,maintaining a proper pitch attitude during turning and banking isdifficult. Another aspect that is generated unrealistically in currentsimulators concerns the way in which an AI unit turns to follow atarget. Computers single-mindedly point an AI aircraft at its target asquickly as possible and follow it as closely as possible. Evenrelatively inexperienced players see that technique as artificial.

The above discussion focuses upon air-combat simulators as an example ofthe class of simulators that employ one or more units or entitiescontrolled by the computer, as well as a simulated unit controlled by aplayer or other user. This genre includes other competitive scenariossuch as BEM (bug-eyed monster) adventure games and NASCAR races. Thereis little difference between simulators for entertainment and those forskill training such as driver education. All but the simplest simulationapplications have less realism than their designers wish for, and theneed for enhanced realism for the computer-controlled AI units is asintense as that for the player-controlled unit.

SUMMARY OF THE INVENTION

The present invention provides systems and methods for enhancing therealism of the computer-controlled AI units of a multi-unit simulatorfor competitive gaming and other applications. That is, thecomputer-controlled units behave more like human-controlled units wouldin the same environment, and their simulated units perform more like thevehicles or other physical units that they model.

An individual situational awareness concept for each AI unit replacesthe global awareness concept of conventional simulators. Rather thanallowing all AI units to observe all other units in the simulatedenvironment all the time, the invention maintains separate lists orrecords for each AI unit indicating which other units that unit can foreach AI unit can react to. An awareness filter maintains the list foreach unit by periodically computing relationships between that unit andthe other units, and adds or drops the other units based at leastpartially upon the computed relationships. Additional optional factorsfor possible inclusion on the awareness list of a unit list are thehistory of past relationships of the other units, and the activity inwhich the subject unit itself is engaged. Situational awareness removesthe apparent omniscience of the AI units that makes them appear lessrealistic.

In order to increase the realism of situational tactics employed by AIunits, another aspect of the invention employs the computedrelationships among the units in the simulated environment to select atactical course of action, for example selecting a target to pursue. Thecomputer periodically generates a score or other evaluation for theother units in the area from the computed relationships, then selects acourse of action, such as designating one of the units as a target,based upon the situational scores of those units. To avoid becomingfixated upon a single objective, reselection of a tactical objective canoccur in response to periodic recalculation of the relationships and thescores. Further, the AI units can adopt individual strategies forpursuing a target or other goal, and can change the strategies short oftheir goals in response to changing tactical situations.

The above and other realism improvements require ways to perform largenumbers of calculations within the available cycle times of a simulator.Evaluating the awareness, targeting, and strategy relationships isespecially burdensome. However, some of the more numerically intensivecalculations can be precomputed and cached, rather than beingrecalculated each time they are needed during a cycle. In addition, thetime interval between successive calculations of the simulation forcertain aspects of the computer-controlled AI units can be individuallyvaried for different ones of the AI units. A situational complexitymeasure is produced for the environment of each unit, and thecalculation cycle or “think rate” for certain parameters is varied inresponse to the complexity of that particular unit at that particularmoment. Accordingly, an AI unit that is closing in to fire at a target,for example, recalculates its control outputs more frequently than whenit is farther away, and more frequently than another AI unit that isfarther away from the action.

Another aspect of the invention simulates the computer-controlled AIunits from a physics engine in the same manner that the human user'sunit is simulated. Rather than employing the guidance outputs of the AIcontrol module directly to set the position, speed, and other parametersof an image of an AI unit, a translator converts the AI operator'sguidance outputs for particular desired parameters into control outputsthat are fed into a physics engine of the same general type as that usedto simulate the player's unit. In this manner, the AI units followexactly the same physical laws as the player's unit. Optionally, thesimulator is the same one used for the player's unit, and differentkinds of AI units can be simulated merely by plugging different physicalmodels into the simulator at different times during the simulationcycles.

Actual physical modeling of the AI units, however, further increases thecomputational load on the simulator to derive realistic control outputsfrom the simpler guidance parameters. Yet another facet of the inventionemploys a relatively simple method for translating the guidanceparameters, but modifies these methods when an AI unit performs extrememaneuvers. One method of this kind is to modify particular controloutput settings during certain types of maneuver, that is, while certainstates or control settings of the AI unit exist. Another method is toadd one or more control inputs to the simulator beyond those availablefor the players to control their units. The guidance controller thenengages these controls in certain tactical situations. In this way,applying more complex control methods only part of the time eases thecomputational load while enhancing realism.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a computer-system environment suitable forhosting the present invention.

FIG. 2 is a block diagram of a simulator for carrying out the invention.

FIG. 3 is a flowchart of the overall operation of the simulator of FIG.2.

FIG. 4, comprising FIGS. 4A-4B, is a situational diagram of multiplesimulated objects.

FIG. 5 describes certain tactical configurations for strategy selectionaccording to the invention.

FIG. 6 illustrates control by body pitch axis according to theinvention.

FIG. 7, comprising FIGS. 7A-7C, is a flowchart showing the operation ofthe simulator of FIG. 2 according to the invention.

DETAILED DESCRIPTION

The following detailed description of preferred embodiments refers tothe accompanying drawings that form a part hereof, and shows specificembodiments of the invention. These embodiments are illustrative, notlimiting. Other embodiments, including other structures and methods,will appear to those skilled in the art. Rather, the scope of thepresent invention is defined only by the appended claims. In particular,those in the art will readily apprehend a wide range of applications ofthe inventive concepts beyond the field of air-combat simulation forentertainment and instructive purposes. The first digit of three-digitreference numerals generally indicates the drawing figure in which thenumeral first appears; two-digit numbers come from FIG. 1.

Illustrative Operating Environment

FIG. 1 provides a brief, general description of a suitable computingenvironment in which the invention may be practiced. The invention willhereinafter be described in the general context of computer-executableinstructions such as program modules executed by a personal computer(PC); however, other environments are possible. Program modules includeroutines, programs, objects, components, data structures, etc. thatperform particular tasks or implement particular abstract data types.Those skilled in the art will appreciate that the invention may bepracticed with other computer-system configurations, including hand-helddevices, multiprocessor systems, microprocessor-based programmableconsumer electronics, network PCS, minicomputers, mainframe computers,and the like. The invention may also be practiced in distributedcomputing environments where tasks are performed by remote processingdevices linked through a communications network. In a distributedcomputing environment, program modules may be located in both local andremote memory storage devices.

An exemplary system for implementing the invention employs ageneral-purpose computing device in the form of a conventional personalcomputer 20, which includes processing unit 21, system memory 22, andsystem bus 23 that couples the system memory and other system componentsto processing unit 21. System bus 23 may be any of several types,including a memory bus or memory controller, a peripheral bus, and alocal bus, and may use any of a variety of bus structures. System memory22 includes read-only memory (ROM) 24 and random-access memory (RAM) 25.A basic input/output system (BIOS) 26, stored in ROM 24, contains thebasic routines that transfer information between components of personalcomputer 20. BIOS 24 also contains start-up routines for the system.Personal computer 20 further includes hard disk drive 27 for readingfrom and writing to a hard disk (not shown), magnetic disk drive 28 forreading from and writing to a removable magnetic disk 29, and opticaldisk drive 30 for reading from and writing to a removable optical disk31 such as a CD-ROM or other optical medium. Hard disk drive 27,magnetic disk drive 28, and optical disk drive 30 are connected tosystem bus 23 by a hard-disk drive interface 32, a magnetic-disk driveinterface 33, and an optical-drive interface 34, respectively. Thedrives and their associated computer-readable media provide nonvolatilestorage of computer-readable instructions, data structures, programmodules and other data for personal computer 20. Although the exemplaryenvironment described herein employs a hard disk, a removable magneticdisk 29 and a removable optical disk 31, those skilled in the art willappreciate that other types of computer-readable media which can storedata accessible by a computer may also be used in the exemplaryoperating environment. Such media may include magnetic cassettes,flash-memory cards, digital versatile disks, Bernoulli cartridges, RAMs,ROMs, and the like.

Program modules may be stored on the hard disk, magnetic disk 29,optical disk 31, ROM 24 and RAM 25. Program modules may includeoperating system 35, one or more application programs 36, other programmodules 37, and program data 38. A user may enter commands andinformation into personal computer 20 through input devices such as akeyboard 40 and a pointing device 42. Other input devices (not shown)may include a microphone, joystick, game pad, satellite dish, scanner,or the like. These and other input devices are often connected to theprocessing unit 21 through a serial-port interface 46 coupled to systembus 23; but they may be connected through other interfaces not shown inFIG. 1, such as a parallel port, a game port, or a universal serial bus(USB). A monitor 47 or other display device also connects to system bus23 via an interface such as a video adapter 48. In addition to themonitor, personal computers typically include other peripheral outputdevices (not shown) such as speakers and printers.

Personal computer 20 may operate in a networked environment usinglogical connections to one or more remote computers such as remotecomputer 49. Remote computer 49 may be another personal computer, aserver, a router, a network PC, a peer device, or other common networknode. It typically includes many or all of the components describedabove in connection with personal computer 20; however, only a storagedevice 50 is illustrated in FIG. 1. The logical connections depicted inFIG. 1 include local-area network (LAN) 51 and a wide-area network (WAN)52. Such networking environments are commonplace in offices,enterprise-wide computer networks, intranets and the Internet.

When placed in a LAN networking environment, PC 20 connects to localnetwork 51 through a network interface or adapter 53. When used in a WANnetworking environment such as the Internet, PC 20 typically includesmodem 54 or other means for establishing communications over network 52.Modem 54 may be internal or external to PC 20, and connects to systembus 23 via serial-port interface 46. In a networked environment, programmodules depicted as residing within 20 or portions thereof may be storedin remote storage device 50. Of course, the network connections shownare illustrative, and other means of establishing a communications linkbetween the computers may be substituted.

The Simulator

FIG. 2 is a high-level diagram of a simulator 200 for carrying out theinvention. The simulator is implemented as an application program 36,FIG. 1. The form of implementation is not important, however; it couldalso take the form of dedicated hardware or a combination of hardwareand software. The following description refers to the simulated aircraftor other unit that is controlled by a human user at the computer as the“player.” The computer-controlled aircraft are “AI” (artificialintelligence) units. When a description focuses upon the control of asingle AI unit, that unit is called the “subject AI unit.” An “opponent”is another unit, the human player or another AI unit, that is the objectof attention of the subject AI unit; if the subject unit is attackingthe opponent, then the opponent is a “target.” The terms “friendly” and“enemy” are in relationship to the unit under discussion. (In thisimplementation, every AI unit knows the status of all other units, andnone are unidentified. There are friendlies and bandits; there are nobogeys. Not yet.)

Simulators transform data representing a current state of the simulatedunits into a subsequent state according to models specifying certaincharacteristics of the units, rules governing actions in the simulatedworld, and—optionally—data concerning other objects in the simulatedenvironment. Transformations occur in a cycle that defines an incrementof real or simulated time. In simulator 200, blocks 210 represent the aconventional portion devoted to the human player. The player manipulatesphysical controls 211 to produce control outputs 212. Controls for anaircraft include items such as ailerons, elevators, and a throttle.Block 214 holds values of data representing the state of the player'sunit for the current simulation cycle. For example, the state of anaircraft includes current bank and pitch angles, speed, flight path, andmany more parameter values. Block 214 also receives the control outputs212. Block 213 is a model comprising values for a large number ofcoefficients and parameters specifying the characteristics of theplayer's aircraft. Many simulators allow the selection of differentmodels for the player's unit.

A simulation program or physics engine 220 implements the rules of thesimulation: in this case, differential equations representing thephysical laws that govern the motion of the simulated units through theair. Most simulators of this type employ difference equations toapproximate the motions over discrete time intervals. That is, engine220 receives state data from block 214 for the difference equationsduring the current simulation cycle. It also receives coefficient andparameter values from model 220 for specifying other parts of theequations. The physics engine plugs all this data into the equations toproduce parameter values and other data representing the next timeperiod of the simulation. This data feeds back to player-state block 214on line 215 to update the state for the next cycle.

Block 230 integrates data for the current player state 214 withenvironmental data from a terrain or scenery database 231 and with datarepresenting other units in the area. Line 232 carries data for allaspects of the simulated scene to a conventional rendering module 233,which prepares a visual image for display 234. Audio data, not shown, isusually also employed to produce engine sounds, gunfire, and so forth.

The AI units in this embodiment simulate other aircraft, both friendlyand enemy, in a combat environment. AI blocks 240 include a separateoperator 241, indicated by the shadowed blocks, for each AI unit withinthe environment. The number of these units can of course change duringthe simulation. The purpose of each operator 241 is to producecontrol-position outputs for that unit. As described more fully below,the control outputs 242 are actual settings for ailerons, elevators, andso forth, rather than simplified conventional commands such as “increasealtitude” or “turn right at a 10°/second rate.” Each AI aircraft or unithas a block 244 for holding its current state, and this block holds thesame kinds of information that block 214 includes for the player'saircraft. That is, each block 244 contains the current values ofparameters representing the status of one of the AI units for thecurrent simulation cycle, such as current bank and pitch angles, speed,flight path, and many more. Rather than producing an abbreviatedsimulation through a simplified process not using physical modeling,each AI state block 244 is essentially the same as player state block214. Each AI unit also has its own model 243, which functions in thesame way as the player's model 213. This state and the model datareceive the same degree of simulation as does the player's aircraft;blocks 243 and 244 employ the same physics engine as that employed forthe player's unit. Physics engine 220 updates the current state of eachAI unit on line 245. The player and the AI units can share the samesoftware or hardware engine, each unit can call an instance of theengine, or the system can provide multiple engines.

The AI states are integrated into the overall view state 230, so thatrendering module 233 produces an image of these units also on display234. The human player reacts to the changing situation in response to avisually displayed scene. The AI units receive global situation data 235representing the positions, speeds, etc. of other units in the area.However, each AI operator can only react to a subset of the global data,as explained below.

The discrete interval of time, or cycle, of the simulation is set by aclock 250. The time rate at which the states 214, 230, and 244 areupdated is referred to as the frame rate 251, typically 20-30 frames persecond. This rate controls physics engine 220, and usually alsorendering module 233 and display 234. A player inputs new controlsettings at any time; the AI operator 241 reads them at the frame rate,and may effectuate them at the frame rate or at a lower rate. However,the AI operators perform some of their calculations at a different ratesat different times, and at different rates for different units, asdescribed in connection with FIG. 3.

FIG. 3 further details a representative operator 300 of the AI operators241, FIG. 2. Situational data 235 maintains a global unit list or otherrecord 301 of the coordinates and other information for all units in thesimulated world, both the player's aircraft and all the AI units. Eachunit has a separate entry in list 301. The operator for each AI unitincludes a separate awareness filter 310 that passes the coordinates ofonly some of the other units to a unit sight list or other form ofrecord 311 for the AI unit that operator 300 manages. Unit sight list311 has a separate entry for each unit that the subject AI is aware of(and thus can react to). Each entry contains the position of the unitand other information taken from its entry in the global unit list. Eachunit sight list entry also includes other tactical information, asdescribed below.

Target selector block 320 can designate the player's aircraft or one ofthe other AI aircraft as a target to be pursued, based upon theirinformation in unit sight list 311. That is, each AI unit chooses atarget only from among the other aircraft that it is aware of, namelythose on its own sight list 311. A tactical condition score 322determines which other unit will be designated as a target.

Strategy selector 321 maneuvers the AI unit to react to the selection ofa target. Selector 321 may also respond to other situational data fromglobal list 235, such as how close the attacking AI unit is to theground, and whether any friendly units are in its line of fire. Strategyselector chooses among a set of possible overall strategies using astored table or list 323 as discussed in connection with FIG. 5.

Control modules 330-360 respond to the foregoing blocks to direct thepath of the subject AI unit in response to the selection of a target anda strategy with respect to that target.

Plan generator 330 formulates a plan for carrying out the selectedstrategy, from which guidance module 340 produces a stream of high-levelguidance commands for executing the selected strategy. The commands aregrouped as follows:

(A) Aileron commands

(1) Desired heading,

(2) Desired bank angle,

(3) Desired roll rate.

(B) Elevator commands

(1) Desired altitude,

(2) Desired pitch angle,

(3) Desired pitch rate,

(C) Throttle commands

(1) Desired airspeed,

(2) Desired throttle percentage.

At each think time, block 340 calculates and issues one guidance commandfrom the aileron group and one from the elevator group; it optionallyalso issues one command from the throttle group. For example, block 340can calculate and issue “Heading 090°” and “Pitch up12°/second” commandsat the same time, but it cannot issue “Heading 090°” and “Bank right60°” at the same time. New guidance commands from block 340 overridepreviously issued commands.

Guidance system 340 receives the guidance commands for that AI unit, andconverts them to low-level control-surface commands 342-346 of the sametype that a human player might input to player controls 211, FIG. 2.These outputs do not, however, directly control the positions of thecontrol surfaces. Instead, a translator 350 converts the command outputs342-345 into settings 351-354 for each of the controls of a realaircraft of the type specified by model 243, FIG. 2. Converter 350includes proportional integral/derivative (PID) controllers of aconventional type for each control of the simulated AI aircraft. Thatis, aileron PID controller 355 converts a command 342 to turn or bankthe AI unit into a changing aileron position 351 according to thephysics of the AI unit. Other PID controllers 356-358 perform the samefunction for other standard aircraft controls, such as elevators,rudder, and throttle. For different types of simulated units, such ashelicopters, race cars, and so forth, the number of types of controlswill differ.

Each controller 355-358 produces an output setting 351-354 for its owncontrol surface position without regard to the state of the othercontrols. A human pilot, however, manipulates the controls as a set. Thestate of one control, and/or the aircraft's reaction to that state,frequently influences the setting of a different control. A turningaircraft, for example, needs back pressure on the stick to maintainaltitude during a turn; the water speed of a boat can have a largeeffect upon the rudder angle required for a turn. Accordingly, modifier360 forms a part of converter 350. Each of the final control positions361-363 for the ailerons, elevators, and rudder are determined by theunmodified intermediate settings 351-353 of any or all of thesecontrols, as well as by other inputs such as 364. Inputs 364 mayrepresent functions of the raw control settings 342-344, previoussettings of the controls, or any other parameters. Modifier 360 can actupon settings 351-353 continuously, or only at certain times or undercertain conditions. Several examples of particular modifications arediscussed hereinafter.

Think-rate module 370 receives the system frame rate time-intervalsignal 252, and determines the rate 351 at which operator 300 performssome of its calculations. That is, certain conditions of the environmentof that AI unit, as determined by strategy selector 321, may causethink-rate generator 370 to output a think-rate signal 371 varying fromthe system frame rate (as high as 30 per second) down to once everysecond or less. In this embodiment, AI units think once per second undernormal circumstances, increasing to three times per second and to theframe rate under certain conditions. Awareness filter 310 operates at afixed 4-second interval, although it too could run at a variable rate ifdesired.

Situational Awareness

One of the hardest tasks of air combat is to see the enemy. Situationalawareness is the knowledge of the position and actions of otheraircraft. Giving the computer-controlled AI aircraft units the same typeof situational awareness as a human pilot greatly enhances the realismof a simulation.

A human pilot can easily spot another aircraft in front. It is moredifficult to see a plane to the side, above, or below. It is almostimpossible to spot a plane in a tail-chase position. Therefore, thevolume around each AI unit is divided into sectors. FIG. 4A shows a topview of four sectors around the AI unit 400, in terms of the angle offits nose (AON) 401. Front sector 410 extends from AON=0° to ±45° off thenose. Side sectors 420 extend from ±45° to ±135°. Rear sector 430occupies remaining quadrant. FIG. 4B shows the sectors in elevation.Side sectors 420 extend from an angle of elevation AOE=0° with respectto aircraft 400 to ±90°; that is from directly above the unit todirectly below it. Front sector 410, however, is divided into threepieces. A direct-front sector 411 extending from AOE=0° to 45° above andbelow the nose. An above-front sector 412 runs from AOE=+45° to +90°,and a below-front sector 413 runs from −45° to −90°. Rear sector 430 isdivided similarly. Direct-rear sector 431 extends from 15° below thenose to 30° above. Above-rear sector 432 runs from AOE=+30° to directlyoverhead, and below-rear sector 433 runs from −15° to −90°. Table Ibelow summarizes the locations of the eight sectors in terms ofhorizontal directions (AON) and vertical directions (AOE).

TABLE I AON AOE Sector −45 to +45 −45 to +45 Direct Front −45 to +45 +45to +90 Above Front −45 to +45 −90 to −45 Below Front  +45 to +135 AnySide +135 to +225 −15 to +30 Direct Rear +135 to +225 +30 to +90 AboveRear +135 to +225 −90 to −15 Below Rear  −45 to −135 Any Side

In this example, the maximum visual range is 8 km from each unit; thatis, each AI unit cannot see another unit, human or AI, farther away than8 km, regardless of its sector.

The sector location determines whether a given AI unit sees another unitor not. If an AI unit sees another unit, its unit sight list 311, FIG.3, contains that other unit's position, altitude, speed, heading, andother properties, such as identity. The way in which the AI unit becomesaware of other units is through a probability table. Table II below isan example of such a probability. The player can specify each AI unitseparately into one of a number of skill categories. Thus, for example,a rookie AI unit always sees another aircraft in its direct-frontsector, that is, with probability 100%. For other units in itsabove-front sector, the probability drops to 70%, and only 1% of otherunits in the direct-rear and below-rear sectors are seen. Theprobability values fall off quickly as units move toward the rear, andthey fall off much more quickly for lower skill levels.

TABLE II Sector Rookie Beginner Intermed. Veteran Ace Direct Front 100100 100 100 100 Above Front 70 75 80 85 90 Side 70 75 80 85 90 BelowFront 60 65 70 75 80 Above Rear 2 5 10 20 25 Direct Rear 1 2 5 10 20Below Rear 1 1 5 10 10

The values in Table II are not absolute probabilities that an AI unitwill see or not see another unit during the period the other unit spendsin a given sector. In real life, the more time a unit spends in anysector, the more likely it is to be spotted. Accordingly, the tablevalues are applied once during every time interval that the AI unit'stactics are reevaluated. This time, derived from the heartbeat interval351, FIG. 3, is called the sighting-round interval. Therefore, if adetection probability is 10%, there is a 10% chance that an AI unit willspot another unit when it enters the sector. After the next sightinground, the cumulative sighting probability rises to 19%; after the thirdround, it becomes 27%, then 34%, and so forth.

Table II contains relative values, not absolute probabilities. Forexample, the table implies that rookies are 50 to 100 times less likelyto see aircraft behind them than those in front of them, and that acesare ten times as likely to see other aircraft directly behind them thanare rookies. Converting these relative numbers to realistic probableperception times requires a calibration factor. One way to achieve thisis to choose a time duration for which an AI unit of a given skill levelwould have a 50% probability of spotting another aircraft in a certainsector, and then to adjust the sighting percentage to yield the desiredaverage detection time. The calibration factor is then applied to allthe percentages in table 450 to produce an absolute probability for eachsector. Its value CF, which can if desired be built into the simulator,is CF=K×(SR/AI)×TP, where SR is the sighting-round interval, AT is theestimated average time to detect an aircraft in a specified sector, andTP is the table percentage for the same sector. The constant K=0.6 is avalue used in place of the desired 50% probability, in order to simplifycalculations. (That is, for small calibration probabilities, it is mucheasier to merely add successive probabilities of success and warp thedesired 50% probability to 60%, than to calculate the actual nonlinearincreases for each round. This approximation fails for largerpercentages, but those cross the threshold quickly no matter how theyare calculated.) Using a calibration factor to convert relativeprobabilities to absolute values in this way makes the average detectiontime independent of the magnitude of the sighting-round interval. Thesighting-round interval can then vary without requiring table 450 to bereprogrammed. In addition, the system can vary the sighting-roundinterval for each AI unit independently of the others, and for the sameAI unit at different times, as described later.

It might be desirable in some cases to employ sectors other than thosedescribed above, or to employ factors other than, or in addition to,relative angle from the AI unit. For example, increasing range couldgradually lower the probability within each sector, rather than merelycutting off visibility abruptly at a maximum range. External factorssuch as sun direction or cloud cover could play a role in awareness.Detection of other units is not the whole of situational awareness.Another way to increase the realism of an AI unit is to introducepersistence of awareness once another unit is detected. Human pilotsremember a once visible aircraft that drops behind their tail. Theycontinue to take evasive action, even though the aircraft is no longervisible. This persistence fades, however; half a minute later the otherunit could be almost anywhere.

Simulating persistence of awareness involves retaining the visibility ofa detected unit for a certain persistence interval, such as 24 seconds.During that time, the AI unit continues to know the properties(position, speed, etc.) of the detected unit. But, when theonce-detected unit has not been sighted for a certain time, the AI unitforgets the presence of the other unit; it drops off the unit sightlist. The AI unit can detect the other unit again, in which case a newtimestamp on its unit sight list starts another 24-second interval. Inthe meanwhile, however, the other unit might have dropped to a lessfavorable sector, making reacquisition less likely. Although retainingfull knowledge of all the properties of the other unit during thepersistence interval gives more knowledge than a human pilot would have,this approximation does lead the AI unit to react in realistic ways.Even more realism could be achieved by clouding the AI's awareness. Forinstance, position and airspeed could be returned as a random valuewhose deviation increases with time since awareness was lost. This wouldcause the AI unit to appear to guess what the out-of-sight unit is doingand to take potentially incorrect actions, as a human pilot would do.

Human pilots also tend to fixate upon a target once they have selectedit. This has the advantage of lowering the probability of losing sightof the target. On the other hand, target fixation lowers the likelihoodof seeing other aircraft in unfavorable positions. Many real dogfightingkills occur when pilots are jumped from behind while concentrating ontheir own attack. It is convenient here to implement target fixation byincreasing the probability that a designated target aircraft passesthrough awareness filter 310, while simultaneously decreasing theprobability that the filter passes other units, human or AI. Multiplyingthe target probability by 1.12 and multiplying the probability of othersby 0.88 achieves a realistic effect in this implementation. Line 312indicates the dependence of these probabilities upon target selection.

Opponent Selection

A complex simulation involving multiple independently controlled unitspresents the problem of how to select one of them as an opponent,target, or other object of attention. At the same time, many-on-manysituations are very interesting, and therefore worth a great deal ofeffort to provide in a simulation. The goal of a target-selection modelis to arrive at a set of rules that cause a computer-controlled AI unitto choose its targets in a manner that appears realistic or human. Someof these rules can be mutually contradictory. This too adds realism; inair-combat simulations, high-density furball situations frequentlyproduce anomalous behavior. In this discussion, the terms “target” and“opponent” are used interchangeably to denote the other unit that an AIunit reacts to at the moment. Although this reaction is usually topursue the other unit, the subject unit may choose to flee, if it shouldcome under attack.

Every simulation application has a different group of heuristic guides,principles, or other considerations, usually gathered from real-lifeexperience. For the air-combat simulation under discussion, the listbelow distills a few of the broader principles followed by good humanfighter pilots.

(1) Choose a target so that the attacking aircraft has a tacticaladvantage, such as speed, altitude, relative position, and/or heading.Both speed and altitude represent stored energy that can be cashed infor tactical advantage; altitude can be traded for speed, and viceversa.

(2) Avoid ganging up on targets that other friendlies have alreadyselected, because that might leave other enemy units unengaged.

(3) Prefer closer targets to those farther away.

(4) Stay with a selected target until it is destroyed, or until thetactical condition becomes unfavorable or dangerous.

(5) In the absence of appealing targets, merely cruise the area until anopportunity presents itself.

(6) Bend the rules when faced with a sudden opportunity or a lucky shot.

Again, these principles are fuzzy, and some are contradictory; but therules should follow them in order to produce realistic behavior on thepart of the AI units.

The concept of a tactical score converts the above principles into ametric that functions as an evaluation of a set of rules. The tacticalscore of a candidate target begins at zero, and adds or subtracts pointsfor the following factors:

(1) ±10 points for every 300 feet of altitude above/below the candidatetarget unit.

(2) ±10 points for every 10 knots of speed above/below that of thecandidate target.

(3) −5 points per 300 feet away from the proposed target.

(4) +100 points if the subject AI follows the target and faces it.

(5) −50 points if the target is behind the subject AI unit.

(6) −100 points if another unit has already selected that candidate as atarget.

The factors include spatial relationships such as distance, bearing, andrelative orientation, and also includes situational aspects such astarget designations by other units. In this implementation, each AI unitis made aware of target selections by the other AI units.

The individual point assignments are arbitrary. Their relative valuesreflect the relative importance of factors such as those describedabove. Higher scores indicate that a target is more attractive thananother candidate having a lower score. For example, the overall scorediminishes considerably when other freindlies have decided to pursuethat target; if the target is otherwise very attractive, it gains enoughpoints to overcome the fact that teammates are already after it. Thepoint system should be comprehensive enough to be realistic, yet simpleenough to be computationally feasible. The only units for which a scoreis calculated are those that the subject AI unit is aware of, namely,those having entries in its unit sight list; these are the only units towhich the subject unit can react.

If no candidate accumulates a score higher than a threshold value, suchas −500 points, the subject AI unit loiters until a favorable potentialtarget presents itself. The subject AI unit selects as a target thecandidate having the highest tactical score. However, in order to addsome unpredictability, the raw scores are randomized somewhat beforebeing presented for final target selection.

Whenever any AI unit does not have a selected target, that unit scansall other enemy units, AI or human, that particular AI unit is aware of.As discussed above, different AI units are aware of different sets ofother units; this restricts the available candidates for target status.

Once an AI unit designates one opponent as a target, strategy selector321 and control modules 330-360 engage and pursue that target. However,target selector 320 continues to evaluate all enemy units on its unitsight list 311 as potential targets. That is, the designated target mayoutmaneuver the AI unit, or another unit may blunder across the AI'spath and present a desirable tail shot. Realistically, target selector320 does not change its mind to gain only a slight advantage; in thisembodiment, selector 320 requires as a threshold that the new potentialtarget have a score 150 points higher than that of a current target.

Strategy Selection

Once target selector 320 of operator 241 of an AI unit has selectedanother unit as an opponent, strategy selector 321 determines theoptimum strategy or tactic to follow. Selection among differentstrategies to achieve an overall objective leads to another aspect ofenhancing the realism of computer-controlled AI units in a simulator.The tactical acumen of conventional computer-controlled AI units istypically low, especially under changing circumstances. It is common tosee AI units ignore a tempting human player when they have alreadyselected another target and refuse to change tactics until that missionhas been accomplished. The present simulator employs strategies, whichform a set of general courses of action to follow. Each AI unit choosesa particular strategy based upon its circumstances and environment. Whenit has selected a strategy, it formulates a particular plan forexecuting that strategy. A plan is an instance of a strategy; a strategylays out a course of action in general terms, while a plan plugs inspecific parameter values for performing the strategy. For example, thestrategy “head-on merge” instructs an AI unit to pass anotherapproaching unit and attempt to gain a tail position on it. A plan forthis strategy might provide detailed orders to gain 700 feet of altitudeon the other unit and pass 500 feet to its right, so as to gain spacefor a turn into a following position. This example reveals theimportance of an ability to change a plan or a strategy before itscompletion. If the oncoming unit, human or AI, turns away after the planhas commenced, a mindless continuation of the original plan until somefixed end condition occurs could well end in disaster for the AI unit.The challenge is to determine the correct conditions for altering orabandoning a chosen plan short of completion. That is, just as targetselector 320 continues to function after target acquisition to determinewhether a designated target should be abandoned, strategy selector 321continues to monitor the tactical situation in order to determinewhether a chosen strategy should be abandoned short of its ultimategoal.

A tactical condition metric determines which of a set of strategies tochoose, and also determines when it is appropriate to abandon thestrategy and select a new one. Although the tactical condition metric isrelatively coarse, it is inexpensive to compute and to implement. Thevalue of a tactical condition metric or score is a function of therelative angular position or orientation, and the relative heading ofthe AI unit with respect to a designated opponent unit.

Conceptually, the invention considers whether the opponent lies in thefront, left, right, or rear quadrant of the subject AI unit. For eachquadrant, the subject AI has one of four possible headings relative tothe opponent: facing away, facing toward, and parallel on the right orleft side. Some of the resulting sixteen tactical conditions are removedby symmetry, and a few more detailed special cases are introduced.

FIG. 5 diagrams twelve tactical configurations 500 that an AI unitemploys to choose a strategy. In the views from above, a filled symbol501 designates a subject AI unit, while open symbol 502 designates itsopponent, either a human player or another AI unit. Configurations 510place the subject AI ahead of its opponent. Configuration 511 has thetwo units approaching each other from the front, while 512 shows theopponent approaching from the rear. Configurations 513 and 514 dividethe opponent's front quadrant into two volumes. In case 513, the subjectAI unit has already crossed the nose of the opponent and is moving awayfrom it; in case 514, the subject unit is still moving toward theopponent's nose. Configurations 520 have the subject AI unit behind itsopponent. Case 521 shows the subject AI in a tail chase, moving towardits opponent, while 522 shows the subject AI moving away. Cases 523 and524 represent stern crossings, where the subject unit is moving towardor away from the opponent. Configurations 530 position the subject AI onthe opponent's flank, with the subject AI moving parallel with theopponent, toward it, parallel in an opposite direction, and away fromthe opponent in configurations 531-534 respectively. Each of theseconfigurations has an equivalent mirror image about its vertical axis,and any possible directional orientation.

Table III shows the strategies programmed for an AI unit in each of thetwelve configurations 500. The first column is the number of theconfiguration in FIG. 5. The second column is a short name of theconfiguration. The third column lists possible strategies for eachconfiguration.

TABLE III Config Engagement Type Strategies 511 Head-on (Merge) (1)Maneuver for separation. (2) Perform head-on attack. 512 Under tailattack (1) Evade. 513 Opponent approaching rear flank (1) Disengage. (2)Evade. (3) Maneuver for tail position. 514 Opponent approaching front(1) Maneuver for separation. flank. (2) Perform head-on attack. 521 Intail-attack position (1) Perform tail attack. 522 Separating behind (1)Maneuver for tail position. (2) Disengage. 523 Approaching opponent'srear (1) Maneuver for tail position. flank 524 Separating from rearflank (1) Disengage. (2) Maneuver for tail position. 531 Flankingparallel (1) Disengage. 532 Closing on opponent's flank (1) Maneuver fortail position. 533 Passing on opponent's flank (1) Maneuver for tailposition. (2) Disengage. 534 Separating on opponent's flank (1)Disengage. (2) Maneuver for tail position.

For example, a head-on engagement configuration 511 can activate amaneuver for separation, intended to position the subject AI 501 in apassing flank configuration 533 and then into configuration 521 for atail attack on the opponent. Alternatively, AI unit 501 can perform adirect head-on attack against opponent 502.

Configuration 512 has the subject AI under tail attack. This is anextremely dangerous position, and the only sensible strategy is to evadethe attacker. An opponent approaching the rear flank, case 513, is lessdangerous. The subject AI can choose to disengage as well as to takemore extreme evasive action. This situation also presents an opportunityfor a third alternative, a possible maneuver toward a tail-chaseposition 521. A table such as 550 can be modified easily, merely byplugging different sets of strategies into column 553.

Where multiple numbered strategies occur for a configuration, a randomselection is made. If desired, however, strategy selection can beassigned or weighted according to a number of situational factors, suchas distance between the aircraft, relative speed, and altitudedifference, as well as remaining fuel and ammunition. For example, whenthe opponent is approaching the rear flank of the subject AI unit(configuration 513, FIG. 5), an altitude or speed advantage can bias theAI unit to maneuver for a tail attack, whereas a tactical disadvantagebiases toward the listed evasion strategy.

Upon each change of configuration, strategy selector 321 chooses a newstrategy, and plan generator 330 formulates a new plan based upon thatstrategy. That is, whenever the subject AI 501 crosses a boundarybetween one configuration 500 and another, one of the strategies listedin column 553 for the new configuration replaces the strategy for theprevious configuration. This approach allows both a natural progressionof tactics and a change in goal in the face of an opponent's tactics. Asthe AI unit follows its plan, the design of the tactical conditions andthe corresponding strategies attempts to bring the subject unit to anadvantageous position and eventually to a firing solution. In thehead-on configuration discussed above, for instance, a separationstrategy is aimed toward ultimately achieving a tail position, and theintermediate flanking configuration preserves this goal—yet recognizesthe possibility that the original goal might not be the best course, andthat disengaging might be preferable under the new circumstances. Forinstance, the opponent might maneuver to deny a head-on engagement. Assoon as this maneuver produces a new configuration, the changed tacticalsituation causes the subject AI to choose a new, more appropriatestrategy, rather than blindly continuing to a completion conditionspecified by the original merge strategy.

Control Interaction

The controls of a vehicle or other guidable object are generallydesigned to control a single aspect of its motion. Fixed-wing aircrafthave ailerons, elevators, a rudder, a throttle, etc. Other vehicles haveother major controls, such as the collective and pitch controls of ahelicopter. To some extent, however, the major controls usually interactwith each other, or produce actions that are not appropriate in certaincircumstances. Many of these cases occur during extreme maneuvers of thevehicle. Although some conventional simulators provide autopilots thatcontrol flight surfaces, none of them perform competently for the rangeof maneuvers and attitudes commonly encountered in combat situations,and none of them will lead an aircraft through extreme situations in thecourse of executing a pilot's guidance instructions.

Block 360, FIG. 2, includes a number of individual modifiers that forcechanges in the control outputs 342-344 in response to their pastsettings, the present or previous settings of other controls, certainstates of a simulated aircraft, and/or other circumstances. The modifiedsettings 361-363 then form the final control outputs 242 of AI operator300.

Human pilots quickly learn to apply back pressure to the stick or yokewhile turning, in order to maintain altitude and increase turn rate.Because elevator output 352 only holds the stick back in response to acommand to gain altitude, one of the modifiers 360 automaticallygenerates a control setting for holding the stick back (or forincreasing an existing stick-back setting) when aileron command 342indicates that the AI unit is in a turn. Calculating the propermagnitude of this interaction is not straightforward, although notcomputationally challenging. In this embodiment, the bank rate 342 froma previous frame is subtracted from that of the current frame. Themodifier then increases the setting of elevator output 352 by a constanttimes the absolute value of this difference, before outputting the finalelevator setting 362.

Another potentially unrealistic action of a AI unit concerns an elevatorcommand 343 to descend during a turn. Another modifier of block 360accordingly disallows any forward-stick setting on output 362 for two ofthree possible circumstances during a turn. If a desired bank anglecommanded by output 342 exceeds the current bank angle indicated byoutput 351 by a threshold amount, the AI unit is entering a turn. Whenthe desired bank angle is about the same as the current angle, the AIcraft is holding a turn. A desired bank angle less than the currentvalue by a threshold amount is rolling out of a turn. Any elevatorsetting 352 indicating a descent is raised to a neutral setting for thefirst two of the above conditions, but not for the third. That is, theAI unit can begin a descent when rolling out of a turn, but not whileentering or holding a turn.

Two further sets of circumstances concerns pitch control while banking.The principle is that pitch commands during a bank do not in factcontrol vertical motion, because the elevators are no longer horizontal.Also, pitch commands during a turn unintentionally control turn rate, inderogation of a turn command on output 342; down elevators kill a turn,while up elevators tighten the turn radius.

First, as the elevators lose more and more pitch effectiveness during abank, the rudder becomes increasingly horizontal, and can produce pitchchanges. An aircraft in a 90° bank can climb and dive with its rudderalone. Accordingly, a modifier function in block 360 reroutes pitchcommands 343 partially to rudder output 353. For current bank anglesless than 10°, no pitch setting affects the rudder output. Above 10°bank, however, the modifier adds to the rudder setting 344 a constanttimes the current bank angle 351 times the desired pitch setting 343, toproduce modified rudder setting 353. This modification also allows theruder interaction to decrease to zero as the AI aircraft returns tolevel flight. Rudder control of pitch can be overdone. Therefore, theabove modifier hard-limits the total increase in rudder setting to 30%of the total settings of the Aaileron, elevator, and rudder angles351-353.

Second, pitch can be controlled by modifying overbanking. Overbank isthe difference between desired and current bank angles, and its value isstored between simulation frames. Another modifier in block 360calculates a function of the current bank 351 and the desired bank 342to produce an overbank correction. Then, whenever elevator output 343requests pitch down, modifier block 360 increases aileron output 351 bythe correction amount at output 361. When output 343 requests pitch up,the modifier calculates another function of the current and desired bankangles to produce a different correction. Block 360 then decreasesoverbank by the second correction. This correction is limited to zerooverbank, however; that is, pitch corrections cannot reverse a desiredbank amount.

In real air-combat operations, human pilots sometimes employ body pitchrate (BPR) control in closing on a target in a tail chase. Window 600shows a simulation area as it would appear to a human pilot closing onan enemy 610 during a turn. The object is to move the enemy left intogunsight crosshairs 601. Because the aircraft is at a significant angleto the horizon 620, direct use of the ailerons and elevatorsindividually move it along the axes 621 and 622 of the worldcoordinates. Attempting to coordinate control-surface commands 342-344to swing the enemy almost directly left in the reference frame 602-603of the AI aircraft produces unrealistic behavior. The AI operator 300,like a human pilot, has two degrees of freedom in directional control,and must choose two guidance instructions—one for each degree offreedom. But, because a dogfight can occur at any attitude relative tothe earth, the AI unit can choose guidance instructions that executerelative to the AI's reference frame, not the earth's.

When the AI is in tail-attack strategy 521, guidance module 340 isenabled to produce BPR guidance commands 346 for moving in a syntheticaxis 602 that is perpendicular to the pilot's body axis 603 within theaircraft. This command is actually a pitch-rate command, and are sent toelevator PID controller 356. It provides very fine control of theattacking unit's attitude when it is lining up on a target.

Guidance module 340 is able to specify one command from each of thegroups (A)-(C) listed above. When the AI is in tail-attack mode, a BPRcommand can be one of the selected commands. For example, block 340 canspecify during the same time interval a setting for BPR and one for bankangle, or a setting for bank angle and one for pitch up. That is,body-pitch control does not replace any other controls; it merely addsone more capability to the set of available commands, allowing morecomplex and realistic action than would otherwise be possible.

Computation Rates

A realistic experience in a real-time simulation depends highly upon ahigh enough frame rate to provide a smooth flight path and quickaircraft response for both the player's unit and the computer-controlledAI units. The calculations required for controlling an AI unit in asimulation are extensive and complex. Performing them in astraightforward manner has the potential for slowing the frame rate ofthe entire simulation. The move toward many-on-many simulations, havinglarge numbers of AI units, compounds this problem.

An AI unit evaluates its tactical condition, performing its target andstrategy selections, its planning, and control settings, during anevaluation interval. As discussed above, each AI unit reevaluates theseparameters repeatedly in order to execute and modify its actions in arealistic manner. The resulting high computational load can be reducedby the insight that the necessity for performing at least some of thecalculations varies from moment to moment with the tactical situation ofeach individual AI unit.

A dynamic AI evaluation interval changes the rate at which an AI thinksabout what it should do during the following evaluation interval,specifically, about what commands 342-346 it should issue for the nextinterval. An AI unit having no currently selected target, or one that isdisengaging from an opponent, can afford to free up computation time forother units by thinking only once per second. On the other hand, liningup a cannon shot on a fast-moving target requires more frequent controlchanges, perhaps as often as once every frame rate. Other situations,such as attempting to close on a tail position, optimize to intermediateevaluation intervals and rates.

For the present purpose and with contemporary computation speeds, a baseevaluation or think interval of one second (1 Hz think rate) isconvenient. Think-rate controller 370, FIG. 3, sets this rate on line371 in the absence of any other rate command from strategy selector 331.As an AI unit enters a more demanding tactical situation, intervalcommand 372 sets a higher rate, say 3 Hz. Thus, for example, an opponentcannot easily throw off an AI pursuer by making a tight turn, becausethe AI unit can adjust its own course more frequently. The mostdemanding; situations, such as preparing to shoot an opponent in tailattack, causes the AI unit operator to reevaluate its parameters andissue new guidance commands for every frame interval. This interval,having a typical rate of 20-30 Hz, allows the highest possiblemaneuverability. When the tactical situation clears somewhat, strategyselector 321 resets the evaluation interval back to a lower rate.

A number of different criteria may serve to set the think rate. In thisembodiment, the criterion is simply the choice of strategy in block 321.Table IV below lists the strategies and their associated think rates. Anew rate becomes effective whenever a new strategy is selected.

TABLE IV Selected Strategy Think Rate Disengage 1 Hz Evade 1 Hz Maneuverfor separation 1 Hz Maneuver for tail position 3 Hz Perform head-onattack Frame rate Perform tail attack Frame rate

Each AI unit has its own think rate, set by its own controller 370 forits own specific tactical condition. In this way, some of the units canmaneuver more quickly without exceeding the overall capacity of the hostcomputer.

Precomputation of tactical information also offers greater computationalefficiency. The calculations necessary for controlling an AI unit suchas an aircraft can be divided into several different types. There aremany parameters concerning units on its sight list that an AI unit mustrefer to frequently and/or in different parts of their control cycle.Many of these computations are expensive; for example, calculations fordetermining angles and distances of other units from each AI unitinvolve the evaluation of trigonometric, square-root, and other complexfunctions. Such calculations should be made only once in each thinkinterval wherever possible.

In this implementation, the following parameters are calculated onlyonce during each think interval for each other AI unit included in thesight list 311 of a subject AI unit:

(1) Tactical configuration 500, FIG. 5,

(2) Quadrant 400, FIG. 4,

(3) Distance from the subject AI unit,

(4) Compass bearing from the subject AI unit,

(5) Relative heading of the other unit from the subject unit.

(6) Angle off nose (AON) of the other unit from the subject unit,

(7) Angle of elevation (AOE) of the other unit from the subject unit,

(8) Tactical score of the other unit.

The simulator caches the results of the above computations in its unitsight list. That is, the entry for one of the other aircraft in thesubject unit's sight list contains all eight of the parameter values forthat aircraft, the entry for a second other aircraft includes all eightparameters for the second aircraft, and so forth. AI code in targetselector 320, strategy selector 331, and any other components ofoperator 300 can access the cached values directly from thecorresponding entry in unit sight list 311. Again, these values arerefreshed once during every evaluation interval of that particular AIunit. Thus, because of the variable evaluation rate, this refresh alsooccurs at a variable rate.

Overall Operation

FIGS. 7A-7C comprise a flowchart showing the operation of AI unitoperators 300, FIG. 3, during a complete cycle or loop 700 of simulator200, FIG. 2. The order of the blocks does not necessarily imply aparticular time sequence. In some cases, the operation requires no timesequence at all; in some other cases, the specific embodiment beingdescribed imposes a time order, but other embodiments might employ adifferent order. That is, loop 700 depicts the logical operation of theinvention, and not its temporal operation. Likewise, the layout andnumbering of the blocks are for convenience only, and do not necessarilyconnote a grouping or other relationship.

Starting at entry point 701, cycle 700 continues to loop by returningthrough completion point 702 at the end of the cycle. Blocks 710 cyclesimulator 200 at its frame rate. At every frame time 251, block 711activates block 712 to update the positions of the player's unit and allthe AI units through physics engine 220 so as to produce the next state.Block 713 cycles through all of the AI operators 300, and then returnsto completion point 702 of the overall loop. Although this cycle isshown within the frame-time loop, the operations in the loop controlledby block 713 are actually interleaved with the frame-rate update, sothat only a portion of them occur during each frame interval. For eachindividual operator 300, block 714 asks whether its think interval hasexpired. Again, the think rate can range from 1 Hz to the frame rate,usually 20-30 Hz, and can differ for different operators. An operatorthat wants to think gets called; operators whose think intervals havenot expired do not get called.

After an operator has been called by block 714, its first task 720 is toupdate the unit sight list 311 of each operator. In this embodiment,list 311 is updated only once every four seconds, regardless of thethink rate; other simulators may desire to update at a different rate,or to key the list interval to the think time or to some other base.When block 721 signals expiration of the sighting interval, block 722cycles through every other aircraft unit, human and AI, having an entryin global unit list 301. Block 723 calculates the distance and angles(AON and AOE) of each unit from the subject unit, in order to determinewhich of the sectors 410-430 contain the other unit, and whether it iswithin visual range. Block 724 reads the skill level of that particularother unit. The human player can set the skill levels of each AI unitindividually, and can thus fight against a mix of enemies (and with amix of friendlies), rather than merely playing others having a singleuniform ability. The added realism for this feature is to force a playerto guess whether any particular enemy lined up for a chancy shot isbetter or worse than the player, and might even turn the tables. Step725 reads a probability from a table such as Table II corresponding tothe sector and skill level of that other unit. Step 726 calibrates andslightly randomizes the table probability to reduce predictability, asdiscussed previously. Blocks 727-729 implement the concept of targetfixation. If block 727 determines that the subject AI unit has alreadydesignated the other unit under consideration from block 722 as atarget, then block 728 increases the table probability by a fixedamount. If the subject AI has not designated any target at all, thetable probability remains unchanged. If it has designated a target butthe unit under consideration is not the target, block 729 decreases thetable probability by a fixed amount. These amounts can differ from eachother, and they may vary with other factors, if desired.

Blocks 740 maintain the unit sight list 311 of an AI unit. Step 741rolls a random number and compares it with the sighting probability todetermine whether or not the other unit under consideration was sightedduring the current sighting interval. If so, block 742 asks whether thisunit is already on the unit sight list. If it is a newly sighted unit,block 743 adds a new entry to the unit sight list, and places in it anidentification of the unit. Block 744 places a timestamp in the entrydenoting when they were sighted. If the current unit is already on thesight list, block 744 updates the timestamp in its entry. Units added toa sight list 311 get to stay there for 24 seconds even if they are notdetected in subsequent sighting cycles. If block 745 sees that thecurrent unit is on the sight list and that its 24-second sighting periodhas expired, block 746 drops its entry. Control then returns to block722 for consideration of the next unit on global unit list 301.

Once during every think interval activated at block 714, blocks 750precompute a number of parameters relating to the units in every unit'ssight list 311. For every AI unit operator 300, block 751 cycles throughall the entries in the sight list for one of the AI units. Block 752calculates the values of all the pertinent parameters, such as relativeangle, heading, distance, etc., for that unit. Block 753 stores thesevalues in the unit sight-list entry for that unit. The entry thus actsas a cache from which other blocks can access certain parameter valueswithout having to recalculate them. Although these values are updated atthe think rate, it is possible to precompute them at another rate, suchas the list update rate, or to base this task upon some other factor,such as a situational complexity measure.

Block 754 calculates the target score for the sight-listed unit underconsideration, as described previously. Again, other embodiments of theinvention may calculate scores for other purposes; the broad purpose ofthe scoring process is to determine the measure of attention that acomputer-controlled AI unit should devote to each of a number of otherunits in the simulation, to determine which other unit or units itshould react to at the moment, based upon one or more criteria. Block755 stores the score in the unit's entry in list 311. To increaserealism by reducing predictability, block 755 can also randomize thescore within a small range.

After block 751 has looped through all the AI units, blocks 760 selectanother unit to which it reacts. Although this unit is called a target,the best strategy could be to run away rather than to try to shoot itdown. Also, it might be desired to consider friendly units as well, forcollision avoidance or other purposes. Block 761 finds the identity ofthe opposing unit having the best target score. Compound decision block762 accepts or rejects the candidate target. If the subject AI unit hasno presently designated target, and if the best score falls below aminimum-interest threshold, then no action is taken. If the score doesexceed the interest threshold, then block 763 designates that unit asthe target. If there is a present target, but all scores have fallenbelow the interest threshold, block 764 drops it. If the present targethas an acceptable score, block 765 determines whether or not a moreappealing target has wandered into the field. If the highest scoreexceeds the score of the present target by a sufficiently highretargeting margin, block 766 redesignates the target; that is, it dropsthe old target and replaces its identity in the sight list with that ofthe new target unit.

Blocks 770 cause the currently running AI operator 300 to react to theselected target unit during the think interval. Block 771 determineswhich of the tactical configurations 500 exists for the current AI unitand its designated target. Step 772 selects a strategy from Table IIIfor the current configuration. If no current target exists, step 772chooses “disengage” as the strategy. If there multiple potentialstrategies for the configuration, block 772 selects among them randomlyor as described above. It is important to note that the strategy forevery AI is reevaluated during every think interval, so that the AI canchange strategies rapidly for changing circumstances. This also allowsless complex strategies to be strung together into more complex goals.Block 773 formulates a specific plan in response to the strategyselection. That is, blocks 771-773 determine a particular course ofaction for the current AI unit.

Block 774 selects the think time for the next cycle 700, in response tothe strategy selection in block 772. If the strategy selected in block772 is a tail-attack mode, then block 776 adds the body-rate (BPR)command to the group of available guidance commands for that AI unit.

Block 777 generates guidance commands for executing the plan. These arestraightforward, except for the BPR unit; more detailed steps for thiscontrol are set out below, after the description of method 700.

Blocks 780 convert guidance commands into control settings appropriatefor the simulated unit, such as aileron, elevator, and rudder positions.Blocks 780 and 790 in this embodiment are executed at the frame rate,rather than at the slower think rate. That is, although high-levelguidance commands might be issued less frequently, the control surfacesof an AI unit change position at the higher frame rate in order toprovide smoother, more realistic action. FIG. 3 shows the actual timingof AI operator functions. The position of these blocks in FIG. 7 showsthe logical flow of the guidance process, rather than its temporal flow.

Block 781 chooses the controls for executing the guidance commands. Ingeneral, elevators control pitch and ailerons control bank and heading.But, as described earlier, elevators can control turn rate in a steepbank, and rudder and ailerons can control pitch. Block 782 cyclesthrough the control surfaces affected by the current guidance commands.Block 783 generates a specific setting for the current control inresponse. Block 784 adjusts the position of the control surface inresponse to the setting; PID controllers 351-354 may implement thisstep. A position commonly has the form of a number, such as “setelevators to +248,” that can be fed directly into the simulator.

Blocks 790 modify control settings from block 784 according to themodification techniques discussed in connection with block 360, FIG. 3.Because some of these employ rates of change or other historical data,block 791 stores previous control settings. In this case, only oneprevious cycle's settings need be kept. Block 792 cycles through each ofthe implemented modification techniques. Each control may have more thanone modification. Block 793 tests the activation conditions for thecurrent modification, and returns to 792 if they fail. If the conditionsare met, block 794 applies the modification method, possibly using datafrom block 791. Block 795 outputs the control settings to the simulator,which reacts to them at the following frame time.

Finally, control returns from block 795 to block 711 for the next loopthrough cycle 700.

As mentioned above, the generation of BPR guidance commands is morecomplex than the others. Referring to FIG. 6, define a “body angle offnose” (BAON) as the angle formed between the opponent 610 and centerpoint 601 in the AI unit's horizontal direction 602, and a “body angleof elevation” (BAOE) as the angle formed between opponent 610 and point601 in the AI's vertical direction 603. The goal of the BPR guidancecommands is to reduce both of these quantities to zero.

The first step is to choose a lead point to aim at. The lead point getsthe attacking AI unit's nose a little bit ahead of the opponent so thata shot fired at the lead point will actually hit the opponent. It alsoallows the attacker to intercept more ably because it heads toward wherethe opponent will be, not where it is now. The lead point is determinedas follows: (1) Calculate how long it would take the attacker tointercept the opponent if the opponent continued on its present course.(2) Divide that intercept time by ten as a lead time. (3) Extrapolatethe opponent's current position, heading and speed by the lead time.This lead point is the extrapolated target position.

The next step is to project the AI unit's position forward in time bythe same amount, i.e., the lead time. This is the extrapolated unitposition.

Next is to calculate the BAON and BAOE between the extrapolated targetposition and the extrapolated unit position. This is a projected valueof what the BAON and BAOE metrics will be; these values are anextrapolated BAON and an extrapolated BAOE. Dividing these extrapolatedangles by the lead time yields their actual change rates.

Taking as much time as possible to line up allows gentler controlmotions. Minimum rates of change for the BAON and BAOE angles dependsupon the distance between the attacking AI and the opponent, and thisdepends upon time to close. Therefore, minimum change rates of BAON andBAOE are the actual BAON and BAOE angles divided by the lead time.Subtracting the actual change rates from the desired change rates giveschange-rate error values for both angles.

To correct for these errors: (1) Issue a bank guidance command to makethe AI unit bank towards its target; that is, roll so that target 610rotates to fall on vertical centerline 604. The bank amount isdetermined by projecting the AI-unit relative position into twodimensions and taking an arctangent. (2) Issue a BPR command to make theattacking AI unit's nose move up or down, in its own reference frame602-603, at the appropriate velocity to make the opponent move alongcenterline 604 until the BAOE becomes zero. The appropriate body pitchvelocity is −1 times the BAOE rate change error. That is, the AI unitrequests a control-surface change to bring the elevation angle ratechange to the desired value.

Conclusion

The above description embraces a number of variations on the basicconcept of enhancing the realistic simulation of objects that interactwith each other in complex ways, including those that are intended toappear human. Those skilled in the art will easily apprehend othervariations and extensions within the described framework, as well asother areas of application of the principles described herein.

What is claimed is:
 1. A method for simulating a subjectcomputer-controlled unit in a simulated environment having at least oneother unit within the environment, the method comprising: periodicallydetermining a tactical condition relating to the subject unit; updatinga first portion of the simulation at a first rate; updating a secondportion of the simulation at a second rate; and varying the second ratein response to changes in the tactical condition.
 2. A computer-usablemedium carrying instructions and data for causing a programmable digitalcomputer to execute the method of claim
 1. 3. The method of claim 1,wherein the tactical condition is determined with reference to the oneother unit.
 4. The method of claim 3, further comprising selecting astrategy for the subject unit, and wherein the tactical condition isresponsive to the selected strategy.
 5. The method of claim 3, whereinthe tactical condition is responsive to a spatial configuration of thesubject unit and the other unit in the simulated region.
 6. The methodof claim 5, wherein the spatial configuration includes the relativebearing between the subject unit and the other unit.
 7. The method ofclaim 5, wherein the spatial configuration includes the relativeheadings of the subject unit and the other unit.
 8. The method of claim1, wherein the first portion of the simulation includes calculatingspatial positions of the subject unit.
 9. The method of claim 8, whereinthe first portion of the simulation includes calculating spatialpositions of the other unit.
 10. The method of claim 1, wherein thesecond portion of the simulation includes determining whether to selectthe other unit as an object of attention.
 11. The method of claim 1,wherein the second portion of the simulation includes determining astrategy for the subject unit to execute.
 12. The method of claim 1,wherein the second portion of the simulation includes issuing guidancecommands for directing the subject unit in the simulated environment.13. A method for performing a simulation of multiple computer-controlledunits in a simulated environment, the method comprising: updating afirst portion of the simulation of all of the units at a first rate;updating a second portion of the simulation of each of the units at oneof a plurality of different second rates; and selecting one of thesecond rates for each of the units independently of the second rates ofthe other units.
 14. A computer-usable medium carrying instructions anddata for causing a programmable digital computer to execute the methodof claim
 13. 15. The method of claim 13, wherein the first rate isconstant.
 16. The method of claim 13, wherein different ones of thecomputer-controlled units have different second rates simultaneously.17. The method of claim 13 wherein the simulation further includes aplayer-controlled unit, further comprising updating the simulation ofthe player-controlled unit independently of the second rate.
 18. Themethod of claim 13, further comprising determining a tactical conditionrelating to each of the computer-controlled units, and wherein thesecond rate for each of the units is responsive to the tacticalcondition.
 19. The method of claim 18, wherein the tactical condition isdetermined separately for each of the computer-controlled units.
 20. Themethod of claim 18, wherein the tactical condition is determinedperiodically.
 21. The method of claim 20, wherein the tactical conditionis determined at different rates for different ones of thecomputer-controlled units.
 22. The method of claim 20, wherein thetactical condition for each of the computer-controlled units isdetermined at the respective second rate of each unit.
 23. A simulatorimplemented on a programmed digital computer for controlling a subjectunit in a simulated environment containing at least one other unit, thesimulator comprising: a rate generator for producing one of a pluralityof different rates; a guidance module for directing the subject unit andresponsive to the rate generator for operating at one of the differentrates; and a selector module for selecting among the different rates inresponse to a predetermined relationship between the subject unit andthe other unit.
 24. The simulator of claim 23, wherein the predeterminedrelationship is a spatial relationship between the subject unit and theother unit.
 25. The simulator of claim 23, wherein the guidance moduleissues guidance commands for the subject unit.
 26. The simulator ofclaim 23, further including a target module for selecting the other unitas an object of attention, the target module being responsive to therate generator for operating at one of the different rates.
 27. Thesimulator of claim 23, including a strategy module for determining astrategy with respect to the other unit, the strategy module beingresponsive to the rate generator for operating at one of the differentrates.
 28. The simulator of claim 27, wherein the strategy moduleincludes the selector module.
 29. The simulator of claim 23, forcontrolling a plurality of subject units, comprising: separate rategenerators for each of the subject units, each rate generator producinga plurality of rates independently of the remaining rate generators;separate guidance modules for directing the subject units and responsiveto respective ones of the rate generators; separate selector modules forselecting among the different rates independently of the remainingselector modules.
 30. The simulator of claim 29, wherein different onesof the rate generators operate at different ones of the ratessimultaneously.
 31. A programmed computer, comprising: input devices foraccepting a sequence of commands from a user for directing a simulatedplayer unit; a plurality of controllers each controlling one of aplurality of subject units, each controller including a rate generatorfor producing one of a plurality of different rates, a guidance modulefor directing the subject unit and responsive to the rate generator foroperating at one of the different rates, and a selector module forselecting among the different rates in response to a predeterminedrelationship between the subject unit and the other unit; a simulatorresponsive to the guidance commands for simulating the units in anenvironment; a rendering module responsive to the simulator forcalculating representations of the units; and output devices responsiveto the rendering module for displaying the representations.
 32. Thecomputer of claim 31, further comprising a set of storage devices. 33.The computer of claim 32, wherein the storage devices hold model dataconcerning the units.
 34. The computer of claim 32, wherein the storagedevices hold terrain data representing the simulated environment. 35.The computer of claim 31, wherein the simulator includes a physicsengine for simulating at least one of the units.
 36. The computer ofclaim 35, wherein the physics engine simulates both the player unit andat least one of the subject units.